Abstract
The historical pattern of the demographic transition suggests that fertility declines follow mortality declines, followed by a rise in human capital accumulation and economic growth. The HIV/AIDS epidemic threatens to reverse this path. We utilize recent rounds of the demographic and health surveys that link an individual woman’s fertility outcomes to her HIV status based on testing. The data allow us to distinguish the effect of own positive HIV status on fertility (which may be due to lower fecundity and other physiological reasons) from the behavioral response to higher mortality risk, as measured by the local community HIV prevalence. We show that although HIV-infected women have significantly lower fertility, local community HIV prevalence has no significant effect on noninfected women’s fertility.
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Notes
Bloom and Mahal (1997) run cross-country regressions of growth of GDP per capita on HIV/AIDS prevalence and find no effect. Papageorgiou C, Stoytcheva P (2008, What Is the Impact of AIDS on Cross-Country Income So Far? Evidence from Newly Reported AIDS Cases, unpublished) find negative effect on the level of income per capita in a similar framework. Werker, E D, Ahuja A, Wendell B (2006, Male Circumcision and AIDS: The Macroeconomic Impact of a Health Crisis, unpublished) instrument HIV/AIDS prevalence by national circumcision rates and show that there is no effect of the epidemic on growth of the African countries.
Kalemli-Ozcan and Turan (2011) shows that Young’s identification from time-series data may not be appropriate given the existing trends in South African data due to abolition of apartheid and the ongoing demographic transition.
Among HIV-positive women, 29 % are widowed, separated, or divorced as opposed to 7 % among HIV-negative women.
The community-level results were produced independently and at the same time in an earlier version of our paper, Juhn C, Kalemli-Ozcan S, Turan B (2008) HIV and Fertility in Africa: First Evidence from Population Based Surveys and a working paper version of Fortson (2009).
In the follow-up paper that uses country by cohort variation, Young (2007) reports a range of coefficients from − 1.54 to − 0.60 (high–low). These coefficients translates into a reduction in fertility of approximately 154–45 % as a country goes from 0 to 100 % prevalence. As discussed in Young (2007), the size of the coefficient appears to be sensitive to the inclusion of the country-specific time trends.
While not directly related to HIV/AIDS, a recent paper by Acemoglu and Johnson (2007) find no effect of life expectancy on level and growth of per capita income. They instrument changes in life expectancy with dates of global interventions in disease prevention. Their results suggest that an increase in life expectancy leads to an increase in population, and fertility responses are insufficient to compensate. It may be the case, however, that many of the countries in their sample have not yet completed the demographic transition. Ashraf et al. (2008) show that the effects of health improvements on income only emerge for half a century after the initial improvement in health.
While the focus of our study is the fertility channel, an equally important question is the effect of HIV/AIDS on human capital investment. A large number of papers cover this topic and generally find substantial negative effects. Meltzer (1992) argues that AIDS raises mortality of young adults, which is going to have the biggest effect on the rate of return on educational investment. He claims that for a 30 % HIV-positive population like Botswana, there would be a 6 % reduction in the rate of return to education relative to no HIV. Bell et al. (2006), using household survey data from South Africa, argue that the long-term economic costs of AIDS could be devastating because of the cumulative weakening from generation to generation of human capital. Fortson (2011), using data similar to ours, shows that children currently growing up in Africa, including non-orphans, will complete 0.3 fewer years of schooling compared to the case of zero HIV prevalence. Akbulut and Turan (2013) show that HIV prevalence in the community impairs the intergenerational human capital transfers even if the mother is HIV-negative.
While their estimates are somewhat higher than other estimates, Gray et al. (1998) is often cited as the study that comes closest to identifying the effect on fecundity. The study interviewed a representative sample of women in their homes and obtained blood samples from 91 % of the women. Most importantly, women did not know their HIV status at baseline because access to testing prior to the survey was not available in the communities surveyed. Contraception and abstinence were also very rare in these communities.
In the 2000s, antiretroviral regimens to prevent mother to child transmission (MTCT) became more widely available even in resource poor countries in sub-Saharan Africa. While a full-scale analysis incorporating differences across regions and time in the availability of these drugs is beyond the scope of our paper, it is important to consider how the omission of this information may bias our results. The availability of drugs designed to reduces MTCT may encourage unprotected sex and higher fertility among HIV-infected women but it may also reduce the precautionary move towards having protected sex among noninfected women making it difficult to forecast a priori the bias in our individual-level regressions. In our community-level regressions, one possibility is that communities with higher infection rates also have more access to drugs (under the plausible scenario that health organizations concentrate their efforts in the most infected areas) and to the extent that the availability of these drugs reduces precautionary motive for protected sex; this would likely lead to a positive bias, confounding the true underlying negative effect of community-level HIV risk on individual behavior.
One important limit of our data is that we observe HIV status at time t while our fertility variables refer to births last year or earlier. One of the implicit assumptions is that infection at time t is a reasonable proxy for infection in previous years.
Each fertility measure has advantages and disadvantages. On the one hand, since HIV status refers to the survey year, number of births last year provides the closest match between treatment and outcome variables. On the other hand, number of births last year is more subject to idiosyncratic noise, and cumulative birth measures may be better indicators of an individual woman’s total fertility. We have also investigated the effects for older women aged 35–49 who may be close to their desired fertility levels except for the marginal child. These results are reported in Appendix Table B-1. The table shows that the results are similar for this group of women.
Urban/rural residence is arguably more endogenous due to migration. In practice, however, we find that including or excluding urban/rural residence has little impact on the size of the HIV coefficient.
The preponderance of zeros as well as the nonnegative and discrete nature of the dependent variable suggests a Poisson specification may be more appropriate. Our Poisson estimates yielded very similar results and are available upon request.
One concern is that there is insufficient overlap in the distribution of covariates. To investigate this issue, we estimated impact of own HIV status on births using propensity score matching. We use the propensity score from the probit estimation (results reported in Appendix Table A-3) and use one-to-one nearest neighbor matching without replacement. We implemented the STATA 9 procedure developed and described in Leuven and Sianesi (2003). We also conducted simple t test on differences in characteristics between HIV-positive and HIV-negative women in our matched sample and did not find statistically significant differences. The results are reported in Appendix Table B-2. The negative impact of HIV is slightly smaller ranging between 15 and 17 %.
Another concern is survivor bias. It may be the case that the HIV-positive women in Table 3 are not a representative sample of all women who ever contracted the virus and that women who contracted the disease earlier had already died. For there to be negative effect on fertility that is due to survivor bias, however, the HIV-positive women who died must have had higher fertility relative to even women who are HIV-negative. There is little to indicate that women who were early contractors of the disease would have had higher than average fertility. For example, those who are more likely to contract the disease are better educated and urban, characteristics that are associated with lower than average fertility.
In results we do not report, we have run the same regression as in Table 4 but on a sample of women who reported positively to “ever had intercourse,” who reported never being tested for HIV and who lived in rural areas where regional HIV prevalence exceeded 15 %.
In a previous version, we defined a community as a country by region by urban/rural residence cell. However, DHS samples are not representative at the disaggregated level. We therefore use country by region cell to define communities in this version while still controlling for urban/rural residence. We thank Jane Fortson for pointing this out to us.
The one coefficient that is negative in sign (column 3) has a 95 % confidence interval of − 0.489 and 0.069. Since the average number of births in the last 5 years is 0.722, the largest negative effect we estimate is a reduction of approximately 67 % (− 0.489/0.722) which is still smaller than the coefficient in Young (2005).
To calculate the TFR for our sample of women with HIV status instead of all the women in DHS survey sample, we follow the method used by the DHS, which uses information on births over the last 36 months for each woman based on the fertility histories. The numerator of each age-specific birth rate is the total number of births over the previous 36 months for women in each 5-year age category based on age at birth. The denominator is the total number of women-years in each 5-year age category. Then we summed up all the age-specific fertility rates and multiply it by 5 (since each woman is present in each age group for 5 years) to end up with the TFR as done by DHS. To adjust TFR for differences in observable characteristics between all and negative HIV women, we run the fertility regression pooling HIV-positive and HIV-negative women as specified in Eq. 1, predict fertility by age group, and add back residuals for HIV-negative women.
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Acknowledgements
We thank Janet Currie, Angus Deaton, Jane Fortson, Emily Oster, Adriana Lleras-Muney, seminar participants at Princeton, University of Texas Austin, the 2007 AIDS Workshop at Amsterdam Institute for International Development, the 2008 Society of Labor Economists Meetings, the 2009 Health and Macroeconomy Conference in University of California at Santa Barbara, the 2009 Health and Macroeconomics Conference in Madrid, and the 2009 American Economic Association Annual Meetings session on “HIV/AIDS and Economic Development” for valuable comments and suggestions.
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Juhn, C., Kalemli-Ozcan, S. & Turan, B. HIV and fertility in Africa: first evidence from population-based surveys. J Popul Econ 26, 835–853 (2013). https://doi.org/10.1007/s00148-012-0456-2
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DOI: https://doi.org/10.1007/s00148-012-0456-2